Universal Algorithms for Clustering Problems

Online algorithms for clustering problems

Approximation Algorithms for Clustering Problems

Approximation Algorithms for Hamming Clustering Problems

We study Hamming versions of two classical clustering problems. The Hamming radius p-clustering problem (HRC) for a set S of k binary strings, each of length n, is to find p binary strings of length n that minimize the maximum Hamming distance between a string in S and the closest of the p strings; this minimum value is termed the p-radius of S and is denoted by %. The related Hamming diameter ...

Approximation Algorithms for Bi-clustering Problems

One of the main goals in the analysis of microarray data is to identify groups of genes and groups of experimental conditions (including environments, individuals, and tissues) that exhibit similar expression patterns. This is the so-called biclustering problem. In this paper, we consider two variations of the biclustering problem: the consensus submatrix problem and the bottleneck submatrix pr...

Genetic Algorithms for Large-Scale Clustering Problems

We consider the clustering problem in a case where the distances of elements are metric and both the number of attributes and the number of the clusters are large. Genetic algorithm approach gives in this environment high quality clusterings at the cost of long running time. Three new efficient crossover techniques are introduced. The hybridization of genetic algorithm and k-means algorithm is ...